02_DEA

In this Analysis step, the Differential Expression Analysis (DEA) is performed using the DESeq2 package.

After performing the DEA, the quality of the data is investigated with a look at the dispersion estimates, size factors and MA plots.

Goal is to find the most significant differential expressed genes. These will be visualized using Volcano plots. To compare the two tissues, the shared significant genes are plotted against each other and a heatmap with hierarchical clustering is performed on all significant genes of both tissues combined followed by a principle component analysis.

loading data

For the DESeq2 analysis, the raw, prefiltered counts will be used.

se.gastroc <- readRDS("./data/Robjects/01_se.gastroc.rds")
se.soleus <- readRDS("./data/Robjects/01_se.soleus.rds")

counts.gastroc <- se.gastroc[rowData(se.gastroc)$filtered,] %>% assay()
counts.soleus <- se.soleus[rowData(se.soleus)$filtered,] %>% assay()
metadata <- colData(se.gastroc)

preparing DDS objects

The DDS objects are created for both tissues separately to investigate the differential expression in regard to the genotype (condition of KO or WT).

# changing to factor (needs DESeq)
metadata$genotype <- as.factor(metadata$genotype)

createDDSObject <- function(counts, metadata) {
  # select sample columns
  reorder_index <- match(rownames(metadata), colnames(counts))
  counts <- counts[,reorder_index] %>% 
    apply(c(1,2), as.integer) # DESeq need normal counts (integer) as input
  
  # Check metadata consistency
  all(rownames(metadata) %in% colnames(counts)) %>%
    assertthat::assert_that(., msg = "metadata and count table do not match")
  
  ## DESeq2 object
  dds <- DESeqDataSetFromMatrix(countData = counts,
                                colData = metadata,
                                design = ~ genotype)
  
  return( DESeq(dds) )
}

# creating DESeq Objects
dds.gastroc <- createDDSObject(counts.gastroc, metadata)
dds.soleus <- createDDSObject(counts.soleus, metadata)

DEA

Performing the Differential Expression Analysis and gathering the results from the DDS objects.

res.gastroc <- results(dds.gastroc, alpha = params$pCutoff, contrast = c("genotype", "KO", "WT"))
res.soleus <- results(dds.soleus, alpha = params$pCutoff, contrast = c("genotype", "KO", "WT"))

Also applying lfcShrink for comparison in volcano plots. Besides that it’s application was not further used.

# lfcShrink is not applied by default

# library(ashr) # using instead of "apeglm", because https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#extended-section-on-shrinkage-estimators

resLFC.gastroc <- lfcShrink(dds.gastroc, contrast = c("genotype", "KO", "WT"), type = "ashr")

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

resLFC.soleus <- lfcShrink(dds.soleus, contrast = c("genotype", "KO", "WT"), type = "ashr")

using 'ashr' for LFC shrinkage. If used in published research, please cite:
    Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
    https://doi.org/10.1093/biostatistics/kxw041

Exploration and Quality Control

count outlier detection with cooks distance

Filtering outliers using the cooks distance is done automatically and p-values are set to NA. Changing (lowering) the minReplicatesForReplace will replace these flagged values.

DESeq2 flags these outliers and removes the corresponding genes completely, if below 7 samples (above would replace the values)

# assuming all NA p-values are the ones filtered out using the cooks distance

print_outliers <- function(res, se){
  # all genes with a `NA` value are exactly those 5 which were also detected by the cooks distance:
  outlier_ids <- res %>% 
    as.data.frame() %>% 
    filter(is.na(pvalue)) %>% 
    rownames()
  
  # a different approach would be finding the genes which have a cooks distance
  # above the cutoff:
  # cooksCutoff <- qf(.99, 2,10)
  # outlier_ids <- which(mcols(dds)$maxCooks > cooksCutoff) %>% names()
  
  # getting counts and metadata
  outlier_metadata.df <- rowData(se) %>%
    as.data.frame() %>%
    select(gene_name, gene_biotype)
  
  counts.df <- assay(se)
  
  # merging df
  outliers.df <-
    merge(outlier_metadata.df, counts.df[outlier_ids,], by = 0)
  return(outliers.df)
}

outliers_gastroc.df <- print_outliers(res.gastroc, se.gastroc) %>% tibble::column_to_rownames(var = "Row.names")
write.csv(outliers_gastroc.df, file = "./code/analysis/02_outliers_gastroc.csv")
outliers_gastroc.df %>% knitr::kable()

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio

	gene_name	gene_biotype	WT_1	WT_3	WT_5	WT_9	WT_11	WT_15	KO_2	KO_4	KO_6	KO_8	KO_10	KO_16
ENSMUSG00000027375	Mal	protein_coding	496	82	87	88	118	71	112	80	70	99	164	200
ENSMUSG00000039579	Grin3a	protein_coding	4	1	5	3	0	3	12	5	11	2	9	142
ENSMUSG00000041607	Mbp	protein_coding	3390	462	524	654	836	536	849	493	456	606	1305	1094
ENSMUSG00000053198	Prx	protein_coding	1190	159	236	327	346	200	313	196	197	237	428	372
ENSMUSG00000056569	Mpz	protein_coding	7949	1168	1229	1562	1657	996	1792	1181	877	1447	2681	2691

print_outliers(res.soleus, se.soleus) %>% knitr::kable()

Row.names	gene_name	gene_biotype	WT_1	WT_3	WT_5	WT_9	WT_11	WT_15	KO_2	KO_4	KO_6	KO_8	KO_10	KO_16
ENSMUSG00000000290	Itgb2	protein_coding	22	47	45	50	72	18	38	457	44	58	66	63
ENSMUSG00000000682	Cd52	protein_coding	8	34	13	18	19	24	10	204	23	15	21	22
ENSMUSG00000000982	Ccl3	protein_coding	0	0	0	3	1	0	1	25	0	0	3	0
ENSMUSG00000001131	Timp1	protein_coding	21	39	25	32	30	38	94	1080	85	33	81	68
ENSMUSG00000002985	Apoe	protein_coding	762	1049	1260	1220	1418	1005	1417	10248	1592	1330	1474	1369
ENSMUSG00000003484	Cyp4f18	protein_coding	2	7	3	8	9	3	17	166	10	15	12	4
ENSMUSG00000003882	Il7r	protein_coding	3	3	1	7	2	0	2	129	3	5	6	6
ENSMUSG00000004707	Ly9	protein_coding	0	0	4	5	8	0	4	122	5	2	0	1
ENSMUSG00000005087	Cd44	protein_coding	183	229	200	236	240	190	324	1750	335	262	414	265
ENSMUSG00000006519	Cyba	protein_coding	135	138	144	132	124	165	177	732	128	166	214	129
ENSMUSG00000015947	Fcgr1	protein_coding	6	5	12	6	10	3	7	143	15	4	10	6
ENSMUSG00000015950	Ncf1	protein_coding	22	51	59	45	71	18	67	497	76	36	75	55
ENSMUSG00000017716	Birc5	protein_coding	7	21	7	8	15	16	16	227	18	19	18	21
ENSMUSG00000018927	Ccl6	protein_coding	83	164	172	135	175	95	206	1248	182	114	204	109
ENSMUSG00000019876	Pkib	protein_coding	2	2	6	2	4	4	4	92	3	3	1	4
ENSMUSG00000019987	Arg1	protein_coding	5	6	2	13	6	1	8	108	6	17	10	9
ENSMUSG00000020120	Plek	protein_coding	17	39	30	32	48	22	36	407	44	32	21	29
ENSMUSG00000020865	Abcc3	protein_coding	14	13	22	16	19	11	17	184	23	19	23	13
ENSMUSG00000021091	Serpina3n	protein_coding	19	22	29	22	22	22	65	404	42	31	49	57
ENSMUSG00000021175	Cdca7l	protein_coding	0	3	2	2	0	0	0	34	2	2	0	2
ENSMUSG00000021886	Gpr65	protein_coding	0	2	5	4	5	5	5	56	5	4	6	3
ENSMUSG00000021998	Lcp1	protein_coding	95	87	97	126	124	73	134	1457	185	129	187	73
ENSMUSG00000022534	Mefv	protein_coding	1	9	1	1	2	0	0	33	2	1	0	2
ENSMUSG00000024300	Myo1f	protein_coding	19	34	19	37	26	22	25	349	42	32	23	25
ENSMUSG00000024397	Aif1	protein_coding	9	6	7	4	9	10	5	128	8	9	15	10
ENSMUSG00000024529	Lox	protein_coding	74	190	110	93	154	108	201	820	161	129	151	152
ENSMUSG00000024675	Ms4a4c	protein_coding	0	0	5	0	2	0	3	26	0	0	3	0
ENSMUSG00000024677	Ms4a6b	protein_coding	13	32	27	24	42	16	21	179	23	18	23	19
ENSMUSG00000024679	Ms4a6d	protein_coding	9	17	12	15	21	13	12	305	22	20	15	6
ENSMUSG00000025044	Msr1	protein_coding	10	7	12	15	22	2	2	358	23	14	15	17
ENSMUSG00000025473	Adam8	protein_coding	30	57	44	53	39	38	49	525	45	38	52	72
ENSMUSG00000025877	Hk3	protein_coding	9	10	7	3	9	2	11	165	9	16	10	10
ENSMUSG00000026358	Rgs1	protein_coding	3	4	0	0	1	0	0	88	0	2	0	1
ENSMUSG00000029304	Spp1	protein_coding	77	47	68	34	78	113	97	4879	96	61	63	62
ENSMUSG00000029373	Pf4	protein_coding	56	69	105	115	111	45	57	846	99	83	75	50
ENSMUSG00000029915	Clec5a	protein_coding	8	10	1	7	4	8	7	86	11	6	10	6
ENSMUSG00000030117	Gdf3	protein_coding	0	0	0	0	0	0	0	129	10	1	3	1
ENSMUSG00000030144	Clec4d	protein_coding	0	0	0	2	1	4	3	110	4	0	5	3
ENSMUSG00000030365	Clec2i	protein_coding	3	3	2	8	2	5	0	44	2	3	4	0
ENSMUSG00000030579	Tyrobp	protein_coding	19	25	45	39	25	18	24	508	34	13	26	20
ENSMUSG00000030786	Itgam	protein_coding	9	36	16	20	39	6	18	450	36	14	36	23
ENSMUSG00000030789	Itgax	protein_coding	24	20	8	9	9	10	17	141	20	19	29	24
ENSMUSG00000031443	F7	protein_coding	1	9	4	3	1	2	16	180	4	3	13	7
ENSMUSG00000031827	Cotl1	protein_coding	76	128	118	132	133	101	134	1350	156	175	174	125
ENSMUSG00000032122	Slc37a2	protein_coding	26	44	36	27	20	29	25	244	35	52	36	22
ENSMUSG00000032218	Ccnb2	protein_coding	14	22	24	4	13	17	27	129	15	26	20	15
ENSMUSG00000032322	Pstpip1	protein_coding	8	29	19	15	26	28	30	186	30	36	34	29
ENSMUSG00000032511	Scn5a	protein_coding	47	49	51	86	108	55	74	542	87	73	92	87
ENSMUSG00000033220	Rac2	protein_coding	17	16	23	27	13	15	23	186	14	20	33	26
ENSMUSG00000033777	Tlr13	protein_coding	3	6	2	10	11	9	17	217	23	12	8	5
ENSMUSG00000034947	Tmem106a	protein_coding	45	41	84	76	80	53	64	391	63	86	97	50
ENSMUSG00000035202	Lars2	protein_coding	1830	4441	2177	1921	1785	1989	7700	1688	1395	1567	1838	1302
ENSMUSG00000035373	Ccl7	protein_coding	3	3	3	14	3	0	5	398	16	17	10	6
ENSMUSG00000035385	Ccl2	protein_coding	10	12	11	43	15	1	16	348	14	12	41	5
ENSMUSG00000036887	C1qa	protein_coding	115	166	220	242	260	142	199	1303	256	195	226	180
ENSMUSG00000037280	Galnt6	protein_coding	4	4	4	2	3	3	4	108	7	1	6	3
ENSMUSG00000037649	H2-DMa	protein_coding	28	39	31	55	20	24	59	227	37	36	47	49
ENSMUSG00000038147	Cd84	protein_coding	8	16	13	8	16	10	4	408	15	9	21	32
ENSMUSG00000038642	Ctss	protein_coding	74	111	107	110	99	50	110	3593	202	87	143	94
ENSMUSG00000040204	Pclaf	protein_coding	8	8	8	7	5	5	3	197	15	13	14	6
ENSMUSG00000040552	C3ar1	protein_coding	14	13	32	36	51	3	48	689	41	20	49	22
ENSMUSG00000040747	Cd53	protein_coding	21	24	32	51	38	15	39	373	52	30	38	38
ENSMUSG00000040809	Chil3	protein_coding	1	0	0	0	6	0	0	66	0	6	4	1
ENSMUSG00000040907	Atp1a3	protein_coding	5	7	3	4	5	6	3	148	8	3	13	12
ENSMUSG00000041431	Ccnb1	protein_coding	4	14	2	1	5	0	5	118	5	1	13	8
ENSMUSG00000043091	Tuba1c	protein_coding	113	105	81	135	172	142	126	606	125	108	119	107
ENSMUSG00000043157	Arl11	protein_coding	4	4	5	3	11	2	8	114	8	1	3	4
ENSMUSG00000044811	Cd300c2	protein_coding	4	7	4	8	3	3	9	178	12	14	12	11
ENSMUSG00000044827	Tlr1	protein_coding	3	1	3	0	1	0	2	78	6	3	9	2
ENSMUSG00000046805	Mpeg1	protein_coding	29	58	47	47	57	35	59	2548	237	45	87	67
ENSMUSG00000047798	Cd300lf	protein_coding	0	3	1	1	2	1	6	126	1	0	2	3
ENSMUSG00000048779	P2ry6	protein_coding	27	15	31	45	52	29	47	274	38	35	55	34
ENSMUSG00000052142	Rasal3	protein_coding	10	5	3	7	6	4	3	54	7	4	4	1
ENSMUSG00000052336	Cx3cr1	protein_coding	12	18	11	10	24	7	27	366	23	8	9	12
ENSMUSG00000055850	Rnf181	protein_coding	41	20	42	23	45	355	48	38	37	35	341	29
ENSMUSG00000056069	Fam105a	protein_coding	29	28	28	22	29	26	30	270	37	30	25	31
ENSMUSG00000063415	Cyp26b1	protein_coding	248	1257	198	94	293	99	4027	59	111	54	37	191
ENSMUSG00000068606	Gm4841	protein_coding	3	0	161	1	1	2	0	45	8	265	37	14
ENSMUSG00000069516	Lyz2	protein_coding	408	806	793	912	972	506	672	12094	1278	687	879	561
ENSMUSG00000070780	Rbm47	protein_coding	3	7	5	2	6	1	4	89	7	5	10	5
ENSMUSG00000073412	Lst1	protein_coding	6	2	7	5	5	3	4	57	2	4	4	6
ENSMUSG00000076258	Gm23935	miRNA	2112	8702	2434	2297	1344	2648	16331	1687	1437	1455	2057	1453
ENSMUSG00000076281	Gm24270	miRNA	22	63	36	28	17	30	152	18	12	14	30	20
ENSMUSG00000078122	F630028O10Rik	antisense	0	15	11	12	13	8	5	97	8	15	9	9
ENSMUSG00000078880	Gm14308	protein_coding	4	1	106	1	0	0	2	4	4	2	3	0
ENSMUSG00000078899	Gm4631	protein_coding	0	0	0	3	0	3	81	0	1	1	1	1
ENSMUSG00000079227	Ccr5	protein_coding	13	5	2	21	21	5	15	262	16	16	14	5
ENSMUSG00000079419	Ms4a6c	protein_coding	3	12	11	8	11	7	9	85	9	6	5	5
ENSMUSG00000095380	Gm23952	miRNA	3	55	7	5	5	7	10	5	6	7	7	8
ENSMUSG00000103349	Gm36888	lincRNA	2	1	14	0	1	3	4	0	0	0	24	0
ENSMUSG00000115230	AC101945.2	lincRNA	0	0	0	0	0	0	2	37	0	1	1	3

size factors

sizeFactors(dds.gastroc)

     WT_1      WT_3      WT_5      WT_9     WT_11     WT_15      KO_2      KO_4 
1.0098829 0.6517416 0.7363933 1.1208488 0.9796707 0.7135590 1.2740994 1.0606543 
     KO_6      KO_8     KO_10     KO_16 
1.0397324 1.2073234 1.0876389 1.6003663 

sizeFactors(dds.soleus)

     WT_1      WT_3      WT_5      WT_9     WT_11     WT_15      KO_2      KO_4 
0.8782302 0.9413594 0.9933758 0.9259800 1.0048036 0.8889040 1.1802624 1.1871833 
     KO_6      KO_8     KO_10     KO_16 
0.9450776 1.1361948 1.1343119 0.9180831 

Dispersion Estimates

gastroc

plotDispEsts(dds.gastroc)

soleus

plotDispEsts(dds.soleus)

MA-Plots

gastroc

plotMA(res.gastroc, ylim = c(-6,6))

soleus

plotMA(res.soleus, ylim = c(-6,6))

top significant diff. genes

topGenes.gastroc <- as.data.frame(res.gastroc) %>%
  tibble::rownames_to_column("GeneID") %>%
  top_n(100, wt = -padj) %>%
  arrange(padj)

topGenes.soleus <- as.data.frame(res.soleus) %>%
  tibble::rownames_to_column("GeneID") %>%
  top_n(100, wt = -padj) %>%
  arrange(padj)

knitr::kable(head(topGenes.gastroc), caption = "gastroc")

gastroc

GeneID	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000050335	4934.2949	6.267396	0.1664065	37.66316	0	0
ENSMUSG00000081249	1378.5460	4.208222	0.1115955	37.70959	0	0
ENSMUSG00000010751	1864.4937	5.610357	0.2014267	27.85309	0	0
ENSMUSG00000032712	5136.5059	4.686418	0.1683204	27.84225	0	0
ENSMUSG00000034855	554.3475	7.276611	0.2735037	26.60517	0	0
ENSMUSG00000037613	960.7558	3.183338	0.1266510	25.13472	0	0

knitr::kable(head(topGenes.soleus), caption = "soleus")

soleus

GeneID	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj
ENSMUSG00000038615	32047.185	-2.5454285	0.0636829	-39.97038	0	0
ENSMUSG00000081249	1651.592	4.5759032	0.1292779	35.39587	0	0
ENSMUSG00000039067	4355.557	-1.1049116	0.0419786	-26.32084	0	0
ENSMUSG00000028932	3190.657	-1.1102258	0.0558536	-19.87741	0	0
ENSMUSG00000006998	7402.617	-0.9196943	0.0468631	-19.62511	0	0
ENSMUSG00000102869	6985.622	-0.9181929	0.0479924	-19.13203	0	0

p-values

# TODO: use ggplot for unified look of plots
hist(res.gastroc$pvalue, main = "gastroc")

hist(res.soleus$pvalue, main = "soleus")

Volcano Plot

volcanoPlot <- function(result, se, pCutoff = 0.01, FCutoff = 1, tissue = character()) {
  gene_names <-
    rowData(se)[rownames(result), c("gene_name"), drop = F]
  results.df <- result %>%
    as.data.frame() %>%
    dplyr::arrange(padj)
  
  # top 10 gene labels for respectively up and down regulation
  labs.up <- results.df[results.df$log2FoldChange > FCutoff, ] %>%
    rownames() %>% .[1:10] %>% gene_names[., c("gene_name")]
  labs.down <- results.df[results.df$log2FoldChange < -FCutoff, ] %>%
    rownames() %>% .[1:10] %>% gene_names[., c("gene_name")]
  selectLab <- c(labs.up, labs.down, "Nfe2l1") %>% unique() # always including "Nfe2l1"
  
  # custom colors:
  keyvals <- ifelse(
    result$log2FoldChange > FCutoff &
      result$padj < pCutoff,
    params$regulated_pal$upregulated,
    ifelse(
      result$log2FoldChange < -FCutoff &
        result$padj < pCutoff,
      params$regulated_pal$downregulated,
      params$regulated_pal$insignificant
    )
  )

  keyvals[is.na(keyvals)] <- params$regulated_pal$insignificant
  names(keyvals)[keyvals == params$regulated_pal$upregulated] <- 'up regulated'
  names(keyvals)[keyvals == params$regulated_pal$insignificant] <- 'nonsignificant'
  names(keyvals)[keyvals == params$regulated_pal$downregulated] <- 'down regulated'
  
  vlc.plt <- EnhancedVolcano(
    result,
    x = 'log2FoldChange',
    y = 'padj',
    title = 'KO vs WT: Nfe2l1 knockout',
    subtitle = ifelse(isEmpty(tissue), "", paste0('tissue: ', tissue)),
    caption = "",
    ylab = expression(paste(-Log[10], p[adj])),
    pCutoff = pCutoff,
    FCcutoff = FCutoff,
    legendPosition = 'right',
    pointSize = 2,
    colCustom = keyvals,
    lab = gene_names$gene_name,
    selectLab = selectLab,
    labSize = 3,
    boxedLabels = TRUE,
    drawConnectors = TRUE,
    max.overlaps = Inf
  )
  
  return(vlc.plt)
}

p <- volcanoPlot(res.gastroc, se.gastroc, pCutoff = params$pCutoff, FCutoff = params$FCutoff, tissue = "gastrocnemius")
ggsave(filename = "plots/02_volcano_gastroc.svg", p)

Saving 7 x 7 in image

ggsave(filename = "plots/02_volcano_gastroc.png", p)
p

p <- volcanoPlot(res.soleus, se.soleus, pCutoff = params$pCutoff, FCutoff = params$FCutoff, tissue = "soleus")
ggsave(filename = "plots/02_volcano_soleus.svg", p)

Saving 19.8 x 19.8 in image

ggsave(filename = "plots/02_volcano_soleus.png", p)

Saving 19.8 x 19.8 in image

p

p <- volcanoPlot(resLFC.gastroc, se.gastroc, pCutoff = params$pCutoff, FCutoff = params$FCutoff, tissue = "gastrocnemius")

Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

ggsave(filename = "plots/02_volcano_gastroc_LFC.svg", p)

Saving 7 x 7 in image

ggsave(filename = "plots/02_volcano_gastroc_LFC.png", p)
p

p <- volcanoPlot(resLFC.soleus, se.soleus, pCutoff = params$pCutoff, FCutoff = params$FCutoff, tissue = "soleus")

Warning: One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...

ggsave(filename = "plots/02_volcano_soleus_LFC.svg", p)

Saving 19.8 x 19.8 in image

ggsave(filename = "plots/02_volcano_soleus_LFC.png", p)

Saving 19.8 x 19.8 in image

p

scatter-plot: most differential genes, both tissues

using the Wald-test stat from the DESeq2 result and filtering on the set FCutoff=1 and pCutoff=0.01 yields the following plot:

apply_cutoffs <- function(deseq.result, colname="stat", FCutoff, pCutoff) {
  res.filtered <- deseq.result %>%
    data.frame() %>%
    filter(padj < pCutoff,
           log2FoldChange > FCutoff | log2FoldChange < -FCutoff) %>%
    dplyr::rename(!!colname := stat) %>%
    dplyr::select(!!colname)
  return(res.filtered)
}

gastroc_res.filtered <- apply_cutoffs(res.gastroc, colname="gastroc", params$FCutoff, params$pCutoff)
soleus_res.filtered  <- apply_cutoffs(res.soleus,  colname="soleus",  params$FCutoff, params$pCutoff)

gene_names <- rowData(se.gastroc) %>% as.data.frame() %>% 
  dplyr::select(gene_name)

# combining Wald-Test data from both tissues and ordering in quadrants
res.combined <- merge(gastroc_res.filtered,
                      soleus_res.filtered,
                      by = 0) %>%
  mutate(diff.exp = case_when(
    gastroc < 0 & soleus < 0 ~ "both down",
    gastroc > 0 & soleus > 0 ~ "both up",
    gastroc < 0 & soleus > 0 ~ "gastrocnemius down,\n soleus up",
    gastroc > 0 & soleus < 0 ~ "gastrocnemus up,\n soleus down",
    TRUE ~ "different"
  )) %>% 
  merge(gene_names, by.x="Row.names", by.y=0)
 
# removing all gene_names except the top_n_genes (sum of absolute Wald-test numbers)
top_n_genes <- 10
top_labels <- res.combined %>%
  group_by(diff.exp) %>% 
  arrange(desc(abs(gastroc) + abs(soleus))) %>%
  filter(row_number() %in% c(1:top_n_genes)) %>% 
  ungroup() %>% 
  .$gene_name

res.combined <- res.combined %>% 
  mutate(gene_name = ifelse(gene_name %in% top_labels, gene_name, ""))

# final plot
p <- ggplot(res.combined, aes(x = gastroc, y = soleus, label = gene_name)) +
  geom_vline(xintercept = 0) + 
  geom_hline(yintercept = 0) + 
  geom_point(aes(color = diff.exp)) +
  # scale_color_manual(values = c("red", "chartreuse1", "bisque", "royalblue")) +
  labs(x = "gastrocnemius", y = "soleus", color = "significantly\ndifferentially\nexpressed") +
  ggrepel::geom_label_repel(max.overlaps = 15) + 
  ggtitle(label = "DEA: t-statistics (Wald test)") +
  theme_bw() +
  theme(
    axis.title = element_text(size = 13),
    axis.text = element_text(size = 13),
    title = element_text(size = 13),
    legend.text = element_text(size = 10)
  )

ggsave(filename = "plots/02_dea_scatter.svg", p)

Saving 7 x 7 in image

ggsave(filename = "plots/02_dea_scatter.png", p)
p

barplot

p <- ggplot(res.combined, aes(x = diff.exp)) +
  geom_bar(aes(fill = diff.exp)) +
  theme_bw()
ggsave(filename = "plots/02_dea_scatter_barplot.svg", p)

Saving 7 x 7 in image

ggsave(filename = "plots/02_dea_scatter_barplot.png", p)
p

boxplots: significant genes in both tissues

boxplot.genes <- function(counts.df, title ="", FCutoff = 1) {
  
  ggplot(counts.df,
         aes(
           # x = as.factor(gene_name),
           x = reorder(gene_name,ensembl),
           y = normalized_counts,
           fill = genotype
         )) +
    geom_dotplot(
      binaxis = 'y',
      stackdir = 'center',
      dotsize = 0.3,
      position = position_dodge(0.8),
      fill = "black"
    ) +
    geom_boxplot(outlier.size = 0.3) +
    scale_y_log10() +
    xlab("Genes") +
    ylab("Normalized Counts") +
    scale_fill_manual(values = params$condition_pal) +
    ggtitle(title) +
    theme_bw() +
    theme(plot.title = element_text(hjust = 0.5)) +
    theme(axis.text.x = element_text(
      angle = 45,
      hjust = 1,
      face = setBold(counts.df$gene_name, c("Nfe2l1"))
    ))
}

prepare_counts <- function(gene_ids, dds, se, reorder_genes = F) {
  counts.top <- counts(dds, normalized = T)[gene_ids, ]
  metadata <- colData(se) %>% as.data.frame()
  gene_names <-
    rowData(se)[, c("gene_name"), drop = F] %>% as.data.frame()
  
  # the prepared counts df for the plot
  counts.plt <-
    data.frame(counts.top) %>%
    tibble::rownames_to_column(var = "ensembl") %>%
    tidyr::gather(key = "samplename",
                  value = "normalized_counts", 2:13) %>%
    merge(metadata, by.x = "samplename", by.y = 0) %>%
    merge(gene_names, by.x = "ensembl", by.y = 0)  %>%
    mutate(genotype = factor(genotype)) %>%
    mutate(genotype = relevel(genotype, "WT")) # "WT" needs to be displayed before "KO"
    
    if (reorder_genes) {
      counts.plt <- counts.plt %>%
        mutate(
          gene_name = forcats::fct_reorder(gene_name, normalized_counts, .desc = T),
          ensembl = forcats::fct_reorder(ensembl, normalized_counts, .desc = T)
        )
    } else {
      idx = match(counts.plt$ensembl, gene_ids)
      counts.plt <- counts.plt[order(idx),] %>%
        mutate(gene_name = factor(gene_name))
      
      counts.plt$ensembl <-
        factor(counts.plt$ensembl, levels = gene_ids, ordered = TRUE)
      counts.plt$gene_name <- factor(counts.plt$gene_name)

    }
  return(counts.plt)
}


# helper function to set the font of a label on the axis to bold
setBold <- function(src, special_labs) {
  # source: https://stackoverflow.com/questions/39694490/highlighting-individual-axis-labels-in-bold-using-ggplot2
  if (!is.factor(src))
    src <- factor(src)
  src_levels <- base::levels(src)
  brave <- special_labs %in% src_levels
  b_vec <- rep("plain", length(src_levels))
  if (all(brave)) {
    b_pos <- purrr::map_int(special_labs, ~ which(. == src_levels))
    b_vec[b_pos] <- "bold"
    b_vec
  } else {
    message("setBold: no matching element found")
  }
  return(b_vec)
}

# actual plot call
for (group in unique(res.combined$diff.exp)) {
  gene_ids <- filter(res.combined, diff.exp == group)$`Row.names`
  prep_counts.gastroc <- prepare_counts(gene_ids, dds.gastroc, se.gastroc, reorder_genes = T)
  prep_counts.soleus <-
    prepare_counts(levels(prep_counts.gastroc$ensembl), dds.soleus, se.soleus, reorder_genes = T)
  gene_ids <- levels(prep_counts.gastroc$ensembl)
  
  p.g <- boxplot.genes(
    counts.df = prep_counts.gastroc,
    title = paste0("Sign. regulated genes: ", group, "\n tissue: ", "gastrocnemius")
  ) #%>% print()
  
  p.s <- boxplot.genes(
    counts.df = prep_counts.soleus,
    title = paste0("\n tissue: ", "soleus")
  ) #%>% print()
  
  ggpubr::ggarrange(p.g, p.s, ncol = 1, nrow = 2)
}

boxplots: top 20 significant genes

setBold <- function(src, special_labs) {
  # source: https://stackoverflow.com/questions/39694490/highlighting-individual-axis-labels-in-bold-using-ggplot2
  if (!is.factor(src))
    src <- factor(src)
  src_levels <- base::levels(src)
  brave <- special_labs %in% src_levels
  b_vec <- rep("plain", length(src_levels))
  if (all(brave)) {
    b_pos <- purrr::map_int(special_labs, ~ which(. == src_levels))
    b_vec[b_pos] <- "bold"
    b_vec
  } else {
    message("setBold: no matching element found")
  }
  return(b_vec)
}

getTopExpressedEnsemblNames <- function(result,
                                        FCutoff,
                                        n,
                                        up = T) {
  .filter <- ifelse(up, `>`, `<`)
  
  subset(result, .filter(log2FoldChange, FCutoff)) %>%
    data.frame() %>%
    filter(baseMean > 100) %>%
    arrange(padj) %>%
    .[1:n, ] %>%
    rownames()
}

boxplot.top <- function(result, dds, se, upregulated=TRUE, n = 20, FCutoff = 1, tissue ="") {
  # getting the top n regulated genes
  names.top <- getTopExpressedEnsemblNames(result, FCutoff=FCutoff, n=n, up = upregulated)
  
  counts.top <- counts(dds, normalized=T)[names.top, ]
  metadata <- colData(se) %>% as.data.frame()
  gene_names <- rowData(se)[, c("gene_name"), drop = F] %>% as.data.frame()
  
  counts.plt <-
    data.frame(counts.top) %>%
    tibble::rownames_to_column(var = "ensembl") %>%
    tidyr::gather(key = "samplename",
           value = "normalized_counts", 2:13) %>%
    merge(metadata, by.x="samplename", by.y=0) %>%
    merge(gene_names, by.x="ensembl", by.y=0)  %>%
    mutate(genotype = factor(genotype)) %>%
    mutate(genotype = relevel(genotype, "WT"))  %>% # "WT" needs to be displayed before "KO"
    mutate(gene_name = forcats::fct_reorder(gene_name, normalized_counts, .desc = T))
  
  direction <- ifelse(upregulated, "up", "down")
  
  ggplot(counts.plt,
         aes(
           x = as.factor(gene_name),
           y = normalized_counts,
           fill = genotype
         )) +
    geom_dotplot(
      binaxis = 'y',
      stackdir = 'center',
      dotsize = 0.3,
      position = position_dodge(0.8),
      fill = "black"
    ) +
    geom_boxplot(outlier.size = 0.3) +
    scale_y_log10() +
    xlab("Genes") +
    ylab("Normalized Counts") +
    scale_fill_manual(values = params$condition_pal) +
    ggtitle(paste0("Top ", n, " ", direction, "-regulated Genes\n tissue: ", tissue)) +
    theme_bw() +
    theme(plot.title = element_text(hjust = 0.5)) +
    theme(axis.text.x = element_text(
      angle = 45,
      hjust = 1,
      face = setBold(counts.plt$gene_name, c("Nfe2l1"))
    ))
}

gastroc

boxplot.top(res.gastroc, dds.gastroc, se.gastroc, upregulated = T, tissue = "gastrocnemius")

boxplot.top(res.gastroc, dds.gastroc, se.gastroc, upregulated = F, tissue = "gastrocnemius")

soleus

boxplot.top(res.soleus, dds.soleus, se.soleus, upregulated = T, tissue = "soleus")

boxplot.top(res.soleus, dds.soleus, se.soleus, upregulated = F, tissue = "soleus")

Venn/Euler-Diagram

significant grouped:

venn.colors <- c(params$tissue_pal, scales::hue_pal()(4))

# numbers for euler diagram
gene_sets <- c(
  "gastrocnemius" = sign_gene_stats$gastroc - sign_gene_stats$shared_sig_genes,
  "soleus" = sign_gene_stats$soleus - sign_gene_stats$shared_sig_genes,
  "gastrocnemius&soleus" = sign_gene_stats$shared_sig_genes
)

# euler: two tissues
p <- plot(
  euler(gene_sets),
  quantities = T,
  legend = list(side = "right"),
  fills = venn.colors,
  main = "significant genes"
)
p

Clustering

Replacing the raw counts with normalized counts for better visual representation of the performed DEA:

counts.gastroc <- counts(dds.gastroc, normalized = T)
counts.soleus <- counts(dds.soleus, normalized = T)

The genes previously determined significant (tissue_res.filtered) in both tissues will be clustered via hierachical clustering and a PCA will be performed by taking the union of the genes.

# obtaining all DE genes, but avoiding duplicates
sign_genes <-
  unique(c(
    row.names(gastroc_res.filtered),
    row.names(soleus_res.filtered)
  )) 
# making sure, that DE genes occur in both tissues
sign_genes <-
  sign_genes[sign_genes %in% rownames(counts.gastroc) &
               sign_genes %in% rownames(counts.soleus)]

counts_sign <- merge(
  counts.gastroc[sign_genes,],
  counts.soleus[sign_genes,],
  by = 0,
  suffixes = c("_gastrocnemius", "_soleus")
) %>% 
  tibble::column_to_rownames(var="Row.names") %>%
  as.matrix()

Heatmap

A zscore is applied to the counts to visualize the differential expression of the normalized counts in a heatmap.

zscore <- function(M) {
  s <- apply( M,1,sd )      # standard deviation
  µ <- apply( M, 1, mean )  # mean
  M.z <- (M - µ) / s        # zscore
  return(M.z)
}

# using the function from "code/visualizations_functions.R"
chm <- plot_combined_CHM(zscore(counts_sign), params=params)

pca: significant genes

PCA is applied to the same significantly DE genes (normalized) as used in the heatmap.

# using the function from "code/visualizations_functions.R"
p <- plot_combined_pca(counts_sign, params=params)
p

save R ojects

save(dds.gastroc, dds.soleus, res.gastroc, res.soleus, file = "./data/Robjects/02_DDS.RData")